With the economic development and living standards improvement, the vehicle, as an indispensable commuting tool, has gradually entered into millions of households. However, the associated driving safety problem brings cloud into the sunny happy life. The number of deaths in traffic accidents in year 2010 in China is 65,225 as reported by the Ministry of Public Security, and is 275,983 as estimated by the WHO model, which is equivalent to a major air disaster every day. The number of casualties caused by traffic accidents in the world is equivalent to 13 times of that of the 911 disaster every year.
In the modern society with advanced science and technology, the populace clearly cannot tolerate successive occurrences of quasi-disasters. The need to drive safely has been unanimously recognized by the society. As driven by the government and the market, the vehicle safety technology has started to prosper. In terms of general classification, the vehicle safety technology may be divided into two main categories, namely, the passive safety and active safety. The passive safety refers to the technology to reduce the loss after a collision to a minimum, mainly the design related to the vehicle structure, seat belt, laminated windscreen glass, collapsible steering column, airbag, etc. However, since the mechanism of the passive protection is not triggered until the collision happens, the number of accidents which may be avoided is limited. The active safety refers to the advance warning technology to avoid the collision, which mainly relates to the perception, understanding, decision, and control on the driver's surrounding environment, such as the lane departure warning (LDW). In comparison, the active safety, for its advantages such as the foresight and the weak association with the vehicle design, has been widely studied, and debuted as the products of the Advanced Driver Assistance System (ADAS).
Vehicle detection technology is one of the essential technical pillars for the active safety. The existing technical solutions mostly adopt a single visual technology, which detects the vehicle by using a method of matching the features in images and is not capable of accurately providing the spatial location and attitude of the vehicle to be detected. Other technical solutions exist, which use laser point clouds as inputs and categorize the features such as the shape and point cloud density before detecting the vehicle. This method mostly requires significant manual adjustments, and has a poor adaptability to scenario changes.